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Start by categorizing the symptom (NaN/Inf, non-convergence, or slow training) and then systematically isolate the cause by checking data, model, loss, and optimization in order. Emphasize a hypothesis-driven approach: make one change at a time, use small-scale experiments, and leverage debugging tools like gradient checking and profiling.
Pro tip: Before diving deep, always run a quick sanity check: try to overfit a tiny subset (e.g., 10 samples) with a simple model. If it fails, the bug is likely in the data pipeline or loss; if it succeeds, the issue is in scaling or optimization.
Reproduce the issue with a minimal setup (e.g., small batch, few epochs) and isolate whether it's data, model, loss, or optimizer related. Use deterministic settings and log key metrics.
Verify data loading, normalization, and augmentation. Ensure inputs are correctly scaled (e.g., CIFAR-10 mean/std) and labels are not corrupted. Visualize a few samples and check for NaNs.
Check for proper weight initialization (e.g., Kaiming/He), activation functions, and architecture. Ensure no exploding/vanishing gradients by monitoring gradient norms.
Confirm loss function is appropriate (e.g., CrossEntropyLoss for classification) and learning rate is reasonable. Try a learning rate finder or reduce LR on plateau. Check for NaN in loss and gradients.
Use PyTorch profiler to identify bottlenecks (data loading, GPU utilization, etc.). Ensure data loading uses multiple workers, pin_memory, and that model is on GPU. Consider mixed precision if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew the broad strokes but got tripped up on the gradient accumulation piece specifically.
Start by outlining the FSDP wrapping strategy, including auto-wrap policies and mixed precision, then detail how optimizer states and gradients are sharded. Finally, explain how gradient accumulation interacts with FSDP and how to implement efficient checkpointing with sharded state dicts.
Pro tip: Mention that FSDP requires careful handling of gradient accumulation to avoid synchronization overhead, and that using `sync_module_states` and `FullStateDictConfig` can simplify checkpointing and loading.
Initialize the distributed process group and set up the FSDP environment, including device placement and mixed precision policies.
Wrap the model using FSDP with appropriate auto-wrap policies (e.g., transformer-based) and configure sharding strategy (e.g., FULL_SHARD).
Use a sharded optimizer (e.g., FSDP's built-in or torch.distributed.optim) and manage gradient accumulation with no_sync context to reduce communication.
Implement checkpointing using FSDP's state_dict APIs, ensuring sharded states are saved and loaded correctly, possibly with FullStateDictConfig for consolidation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the context: sparse gradients arise in models with large embedding tables or Mixture-of-Experts, where only a small fraction of parameters receive updates per step. Then describe a concrete implementation using sparse all-reduce (e.g., all-gather of indices and values, or reduce-scatter with sparse tensors) and compare it to dense all-reduce in terms of communication volume, latency, and complexity. Finally, discuss when sparsity actually pays off—typically when sparsity is high (>90%) and the system is communication-bound—and mention practical challenges like load imbalance and straggler effects.
Pro tip: Emphasize that sparse all-reduce is not a drop-in replacement: it often requires custom communication primitives and careful handling of duplicate indices across workers. Also note that in practice, many frameworks (e.g., PyTorch DDP) still densify gradients for simplicity, so you must justify the engineering effort with a clear cost-benefit analysis.
Identify where sparse gradients occur (e.g., embedding layers, MoE) and quantify sparsity level (e.g., 99% zeros). Explain that sparsity is per-worker and may vary, leading to load imbalance.
Outline a communication pattern: each worker sends only non-zero (index, value) pairs. Use all-gather to collect all indices/values, then locally aggregate duplicates. Alternatively, use reduce-scatter with sparse tensors if supported.
Analyze tradeoffs: sparse reduces communication volume when sparsity is high, but adds overhead from metadata, irregular communication, and potential load imbalance. Dense is simpler and more efficient when sparsity is low or hardware is optimized for dense ops.
Sparse all-reduce is beneficial when communication is the bottleneck, sparsity is high (>90%), and the model has large, sparse parameters. It may not be worth it for small models or when compute-bound.
Address challenges: stragglers, duplicate indices, and framework support. Alternatives include gradient compression (e.g., top-k), which can be more general and easier to implement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing custom kernels as a last-resort optimization after exhausting high-level options like torch.compile, fusion, and library kernels. Then describe the specific circumstances (e.g., memory-bound ops, unsupported patterns, fusion opportunities) and outline a rigorous verification process that includes both correctness checks and performance profiling.
Pro tip: Emphasize that you always profile first to identify the bottleneck, and that you consider the maintenance cost and portability trade-offs before writing a custom kernel. Mention that you often prototype in Triton for faster iteration and only drop to CUDA when necessary.
Profile the training loop to find where time is spent (e.g., using PyTorch profiler, Nsight). Determine if the bottleneck is a specific operation that is memory-bound, launch-bound, or not well-optimized in existing libraries.
Before writing a custom kernel, check if existing solutions like torch.compile, cuDNN, or fused optimizers can address the issue. Consider the trade-off between potential speedup and development/maintenance cost.
Write a custom kernel when there's a clear opportunity: e.g., fusing multiple element-wise ops to reduce memory traffic, implementing a novel operation not supported by libraries, or optimizing for specific hardware features (e.g., tensor cores, sparsity).
Compare outputs against a reference implementation (e.g., PyTorch eager) using unit tests with random inputs, checking for numerical equivalence within tolerance. Also test edge cases and gradients if applicable.
Benchmark the kernel in isolation and within the full training loop, measuring throughput, latency, and memory usage. Use proper warm-up and multiple runs to account for variance, and compare against the baseline.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.